According to Govenor Holcomb, the state of Indiana will be using hospitalization numbers as their most important indicator for this pandemic.
Because hospitalizations are smoother than cases or deaths, we present the data with a line graph below:
The percentage of tests coming back positive is one way to measure the prevalence of testing.
Note that the y-axis scales of these graphs are different than the state-level data.
Note that if the number of deaths below is more than the official count according to Kosciusko county, that is most likely because our data source is also counting non-residents of Kosciusko county who have died in Koscisusko county.
The coloring scheme for the map below is based on a combination of new cases and new deaths per captita in the past week. The coloring scheme is relative, it will always color the worst county pure red.
Below are the six counties in Indiana with the worst combination of new confirmed cases and new confirmed deaths over the past week.
Note that the y-axis scales of these graphs are different than other sections.
The first chart is a comparison of smoothed weighted averages. The second chart is the actual data.
The chart below does not contain two outliers for Cass County. On April 26th, Cass County reported 271 new cases (575 per 80,000), and on April 27th, Cass County reported 439 new cases (932 per 80,000).
The first chart is a comparison of smoothed weighted averages. The second chart is the actual data.
Note that the y-axis scales of these graphs are different than other sections.
The first chart is a comparison of smoothed weighted averages. The second chart is the actual data.
The first chart is a comparison of smoothed weighted averages. The second chart is the actual data.
Note that the y-axis scales of these graphs are different than other sections.
The first plot is a comparison of smoothed weighted averages. The second is the actual data.
Because hospitalizations are smoother than cases or deaths, we present the data with line graphs below:
The first plot is a comparison of smoothed weighted averages. The second is the actual data.
Looking at the percentage of tests that come back positive can signal which states might have low case numbers because of lack of testing.
For example, Michigan had a much higher death rate per capita than Illinois in mid-April, even though their case numbers per capita were only a little higher in late March and early April. Michigan’s high postive test rate late March and early April suggests that Michigan had worse testing than Illinois during that time.
The first plot is a comparison of smoothed weighted averages. The second is the actual data.
Warning: the plots below contain several outliers where
The coloring scheme for the map below is based on a combination of average hospitalizations and new deaths per captita in the past week. The coloring scheme is relative, it will always color the worst state pure red.
The first chart is a comparison of smoothed weighted averages. The second chart is the actual data.
Because hospitalizations are smoother than cases or deaths, we present the data with line graphs below:
The first chart is a comparison of smoothed weighted averages. The second chart is the actual data.
The coloring scheme for the map below is based on a combination of new confirmed cases per capita and new deaths per captita in the past week. The coloring scheme is relative, it will always color the worst county pure red.
Data Sources:
Guidotti, Emanuele, and David Ardia. 2020. “COVID-19 Data Hub.” https://www.researchgate.net/project/COVID19-Data-Hub.
Smith, Mitch, Karen Yourish, Sarah Almukhtar, Keith Collins, Danielle Ivory, and Amy Harmon. 2020. “Coronavirus (Covid-19) Data in the United States.” The New York Times. https://github.com/nytimes/covid-19-data.